The PROFIT Framework: How To Measure ROI For Data Analytics Projects
The value of data analytics (DA) and artificial intelligence (AI) are widely recognized, but the challenge lies in precisely quantifying their ROI. The dynamic nature of DA and AI means that their outputs, while effective for many use cases, defy easy valuation.
The Importance of Measuring ROI for Data Analytics Projects
Corporate leaders are often faced with the critical question of how to determine and justify the ROI of technology and talent expenditures while balancing the substantial costs of setup and maintenance against anticipated benefits. This is always a good question to answer when there is hype around new technologies and modernization efforts, such as generative AI.
Gaining Stakeholder Buy-In
In many industries, the C-Suite may buy in on particular use cases with clear impact on the bottom line, such as reducing empty miles for logistics companies to increase overall profitability. However, they may not always see how analytics initiatives impact the overall company health or customer satisfaction and market perception.
Key Stages of ROI Evaluation
There are several key stages for conceptualizing, capturing, and communicating ROI to stakeholders throughout a DA or AI program’s life cycle. These stages involve creating compelling business cases, securing funding, selecting the right KPIs, establishing ROI, setting targets, and measuring and monitoring progress against the set targets.
The PROFIT Framework
The PROFIT framework provides a structured methodology for executives to navigate the complexities of DA investments, ensuring that the benefits of analytics investments are realized and validated. It aims to ensure that every dollar spent is accounted for and that the financial advantages of such investments are realized and validated.
Dimensions of the PROFIT Framework
The PROFIT acronym encapsulates six important areas of contribution for DA and AI, ensuring that the benefits of analytics investments are comprehensively assessed. These areas include bolstering the company’s market share, managing and mitigating risks, improving operations and automating processes, positively impacting the company’s financial health, creating cutting-edge products and services, and fostering confidence among stakeholders in the company.
Implementing the PROFIT Framework
Implementing the PROFIT framework involves identifying gains across the six dimensions and ensuring that ROI captures true value from DA use cases. The stages within the framework enable companies to enhance market presence, build customer trust, overcome data literacy challenges, integrate DA and win organizational support, and quantify the impact of their data-driven strategies.
Conclusion
The PROFIT framework provides a comprehensive analysis of the ROI for DA projects. With this method, companies can enhance their market presence, build customer trust, overcome data literacy challenges and skepticism, integrate DA, and win organizational support. In short, the framework serves as a guide, demonstrating the tangible benefits of DA investments augmented by AI and helps businesses quantify the impact of their data-driven strategies.
Source: forbes
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